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English | 简体中文

PaddleDetection

The goal of PaddleDetection is to provide easy access to a wide range of object detection models in both industry and research settings. We design PaddleDetection to be not only performant, production-ready but also highly flexible, catering to research needs.

Now all models in PaddleDetection require PaddlePaddle version 1.6 or higher, or suitable develop version.

Introduction

Features:

  • Production Ready:

    Key operations are implemented in C++ and CUDA, together with PaddlePaddle's highly efficient inference engine, enables easy deployment in server environments.

  • Highly Flexible:

    Components are designed to be modular. Model architectures, as well as data preprocess pipelines, can be easily customized with simple configuration changes.

  • Performance Optimized:

    With the help of the underlying PaddlePaddle framework, faster training and reduced GPU memory footprint is achieved. Notably, YOLOv3 training is much faster compared to other frameworks. Another example is Mask-RCNN (ResNet50), we managed to fit up to 4 images per GPU (Tesla V100 16GB) during multi-GPU training.

Supported Architectures:

ResNet ResNet-vd 1 ResNeXt-vd SENet MobileNet HRNet Res2Net
Faster R-CNN x
Faster R-CNN + FPN
Mask R-CNN x
Mask R-CNN + FPN
Cascade Faster-RCNN
Cascade Mask-RCNN
Libra R-CNN
RetinaNet
YOLOv3
SSD
BlazeFace
Faceboxes

[1] ResNet-vd models offer much improved accuracy with negligible performance cost.

More Backbones:

  • DarkNet
  • VGG
  • GCNet
  • CBNet

Advanced Features:

  • Synchronized Batch Norm: currently used by YOLOv3.
  • Group Norm
  • Modulated Deformable Convolution
  • Deformable PSRoI Pooling
  • Non-local and GCNet

NOTE: Synchronized batch normalization can only be used on multiple GPU devices, can not be used on CPU devices or single GPU device.

Tutorials

News: Documentation:https://paddledetection.readthedocs.io

Get Started

Advanced Tutorial

Model Zoo

License

PaddleDetection is released under the Apache 2.0 license.

Updates

v0.2.0 was released at 02/2020, add some models,Upgrade data processing module, Split YOLOv3's loss, fix many known bugs, etc. Please refer to 版本更新文档 for details.

Contributing

Contributions are highly welcomed and we would really appreciate your feedback!!